code: def evaluate(self, X, Y):
      top_k_list = [len(l) for l in Y]
      Y_ = self.predict(X, top_k_list)
      Y = self.binarizer.transform(Y)
      averages = ["micro", "macro", "samples", "weighted"]
      results = {}
      for average in averages:
          results[average] = f1_score(Y, Y_, average=average)
      # print('Results, using embeddings of dimensionality', len(self.embeddings[X[0]]))
      # print('-------------------')
      print(results)
      return results
  
file_path: OpenNE/src/openne/classify.py
imports: from __future__ import print_function
  import numpy
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.metrics import f1_score
  from sklearn.preprocessing import MultiLabelBinarizer
  from time import time
label_desc: Use image node_id 3 for calling the Classifier obj's underlying member method code with example usage: obj.evaluate(X, Y) and returns: results